Aug 2026· 2026 6th International Conference on Emerging Smart Technologies and Applications (eSmarTA)· pp. 1-9· 0 citations· 54 references
Abstract
Timely and dependable information exchange is essential for large-scale unmanned aerial vehicle (UAV) swarms to coordinate under their fast motion, intermittent links, and limited energy on board. However, swarm deployments increasingly must contend with spectrum contention and jamming, as well as a lack of dependable infrastructure, which can reveal the shortcomings of traditional radio-frequency (RF) networking. This paper presents a synthesized overview of communication technologies and networking architectures for UAV swarm operations in FANETs. Representative studies were identified by a structured search and screening process across major venues of scholarly output, which are synthesized using a cross-layer lens including physical links, medium access, routing, information-centric networking, learning-enabled adaptation, and security. In this article, We compare RF/cellular with emerging high-capacity links including millimeter-wave and free-space optical communication, and then show how routing/indirection and content/function-centric paradigms (NDN/NFN) can mitigate fragility imposed by reliance on brittle end-to-end paths. Lastly, we analyse learning-based control (especially multi-agent reinforcement learning) for communication-aware mobility and resources management, as well as security approaches for contested settings. The resulting design perspective highlights recurring trade-offs among reliability, latency, throughput, energy, and integrity, and identifies practical research directions toward more robust and deployable swarm communication systems.
This paper reviews UAV swarm ad-hoc network communication technology for emergency scenarios, examines the technical characteristics and applicability boundaries of three network architectures, and surveys recent advances in routing and medium access, intelligent networking optimization, and transmission and security assurance.
Yihang Ren, Hua-Tao Zhu, Jie Zhang· International Journal of Eme...· 0 citations
—Unmanned aerial vehicle (UAV) swarm-assisted integrated sensing and communication (ISAC) networks are a crucial technology for providing communication and sensing services in emergency rescue scenarios without base station support. However, the strong coupling between communication and sensing resources in such networks fundamentally limits the communication and sensing performance of ISAC systems. This paper jointly optimizes spectrum allocation, UAV association and deployment to maximize average system throughput while ensuring localization accuracy in such networks, where sensing is realized through localization. We begin by deriving an analytical expression for localization accuracy, which explicitly captures the joint effects of link quality and anchor geometry under shared communication-localization spectrum resources. We then formulate average system throughput maximization as a mixed-integer nonlinear and non-convex optimization problem with the constraints of localization accuracy, sub-channels, UAV association, UAV deployment and signal-to-interference-plus-noise ratio. We further develop an alternating iterative optimization method to solve this complex optimization problem. Within this method, a particle swarm optimization-based method is developed to jointly optimize spectrum allocation and UAV association, and a dueling double deep Q-network-based method is further employed for UAV deployment optimization. Finally, extensive simulation results are presented to validate the efficiency of our optimization method, and also to illustrate how key parameters influence average system throughput and localization accuracy.
Zhuo-Jia Yang, Wei Su, Bin Yang et al.· IEEE Transactions on Mobile...· 0 citations
A comprehensive and structured review of methods for MACNs, with particular emphasis on AI-driven solutions and their relationship to classical and hybrid alternatives, and offers insights into the design of AI-driven MACNs that are efficient, scalable, and adaptive to evolving network and service demands.
Shafkat Khan Siam, Muhammad Yeasir Arafat, Muhammad Morshed Alam et al.· Artificial Intelligence Revi...· 0 citations
This survey provides a systematic review across six interconnected domains—channel estimation (CE) and beam tracking, throughput maximization, weighted sum rate (WSR) and sensing co-optimization, delay and age of information (AoI) minimization, energy efficiency (EE), and PLS—each supported by a structured comparative table covering over 80 methodologies.
Manzoor Ahmed, Syed Tariq Shah, A. A. Nasir et al.· IEEE Open Journal of the Com...· 1 citation
Driven by the vision of a thriving low-altitude economy and aiming to provide on-demand services for diverse entities, this paper investigates an integrated sensing and communication (ISAC)-enabled low-altitude wireless network (LAWN). Benefiting from flexible mobility and cost-effective cooperative deployment, multiple ISAC-enabled uncrewed aerial vehicles (UAVs) are emerging as an ISAC paradigm for on-demand deployment in LAWN. However, due to the complex inter-UAV interference and resource coupling in LAWN, it is difficult to properly coordinate different constrained resources, including spatial deployment, energy, and wireless channels, to simultaneously meet the sensing and communication requirements. To address these challenges, this paper formulates a sensing–communication optimization (SCO) problem in LAWN by jointly optimizing subcarrier allocation, transmit power allocation, and three-dimensional (3D) UAV deployments to maximize network utility while satisfying quality of service (QoS) requirements for multiple users and target sensing mutual information (MI) requirements. To enable efficient solutions, we propose a hierarchical optimization approach that vertically decouples the SCO problem into two subproblems: a top level employing a Gibbs Sampling–based multi-UAV 3D deployment algorithm for efficient exploration and deployment optimization, and a bottom level performing resource allocation via a dual-based joint power and subcarrier allocation algorithm. Simulation results demonstrate that the proposed approach achieves a favorable trade-off between communication and sensing and significantly enhances the overall performance and adaptability of the LAWN.
Cheng Ma, Zewei Jing, Qinghai Yang et al.· IEEE Transactions on Wireles...· 0 citations
High-performance networking is essential for Unmanned Aerial Vehicle (UAV) swarms to accomplish complex, coordinated missions. A central challenge in UAV swarm networking is managing concurrent multi-hop transmissions, where traditional protocols often struggle due to routing path conflicts and co-channel interference. To address this, we propose a novel multi-agent reinforcement learning (MARL)-based cross-layer transmission framework that maximizes system throughput by jointly optimizing network-layer routing, link-layer resource allocation, and UAV trajectories. We decouple this complex joint optimization problem and solve it with a routing-prioritized iterative scheme. For the routing sub-problem, an MARL approach is designed for agents to collaboratively plan concurrent routing paths. The non-convex resource allocation and trajectory sub-problems are handled using successive convex approximation (SCA). Experimental results demonstrate that our proposed framework significantly outperforms existing benchmarks in system throughput, end-to-end delay, and packet delivery ratio.
Yang Shen, Bing Li, Rong-Qing Zhang· IEEE Transactions on Wireles...· 0 citations
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